Physicians are overwhelmed with many different drugs and the need to know a lot of information about all of them. That is, however, almost impossible in the fast evolving area of pharmaceutical industry. Although many data sources about drugs are published on the Web, structured or unstructured, it is very time consuming to search through them. In this paper we identify these data sources according to information needs of physicians. We show that they can be relatively easily integrated using the Linked Data principles and, in case of unstructured data, NLP methods. An application on the top of the integrated data sets is presented as a possible tool for clinical decision support.

In this paper we present a fuzzy system which provides a fuzzy classification of textual web reports. Our approach is based on usage of third party linguistic analyzers, our previous work on web information extraction and fuzzy inductive logic programming. Main contributions are formal models and prototype implementation of the system and evaluation experiments.

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In this paper we present a method for semantic annotation of texts, which is based on a deep linguistic analysis (DLA) and Inductive Logic Programming (ILP). The combination of DLA and ILP have following benefits: Manual selection of learning features is not needed. The learning procedure has full available linguistic information at its disposal and it is capable to select relevant parts itself. Learned extraction rules can be easily visualized, understood and adapted by human. A description, implementation and initial evaluation of the method are the main contributions of the paper.